One problem encountered in visually tracking objects is the ability to maintain a representation of target appearance that has to be robust enough to cope with inherent changes due to target movement and/or camera movement. Methods based on template matching have to adapt the model template in order to successfully track the target. Without adaptation, tracking is reliable only over short periods of time when the appearance does not change significantly.
However, in most applications, for long time periods the target appearance undergoes considerable changes in structure due to change of viewpoint, illumination or occlusion. Methods based on motion tracking where the model is adapted to the previous frame, can deal with such appearance changes. However, accumulated motion error and rapid visual changes make the model drift away from the tracked target. Tracking performance can be improved by imposing object specific subspace constraints or maintaining a statistical representation of the model. This representation can be determined a priori or computed online. The appearance variability can be modeled as a probability distribution function which ideally is learned online.
An intrinsic characteristic of the vision based tracking is that the appearance of the tracking target and the background are inevitably changing, albeit gradually. Sine the general invariant features for robust tracking are hard to find, most of the current methods need to handle the appearance variation of the tracking target and/or background. Every tracking scheme involves a certain representation of the two dimensional (2D) image appearance of the object, even though this is not mentioned explicitly.
One known method using a generative model containing three components: the stable component, the wandering component and the occlusion component. The stable component identifies the most reliable structure for motion estimation and the wandering component represents the variation of the appearance. Both are shown as Gaussian distributions. The occlusion component accounting for data outliers is uniformly distributed on the possible intensity level. The method uses the phase parts of the steerable wavelet coefficients as features.
Object tracking has many applications such as surveillance applications or manufacturing line applications. Object tracking is also used in medical applications for analyzing myocardial wall motion of the heart. Accurate analysis of the myocardial wall motion of the left ventricle is crucial for the evaluation of the heart function. This task is difficult due to the fast motion of the heart muscle and respiratory interferences. It is even worse when ultrasound image sequences are used.
Several methods have been proposed for myocardial wall tracking. Model-based deformable templates, Markov random fields, optical flow methods and combinations of these methods have been applied for tracking the left ventricle from two dimensional image sequences. It is common practice to impose model constraints in a shape tracking framework. In most cases, a subspace model is suitable for shape tracking, since the number of modes capturing the major shape variations is limited and usually much smaller than the original number of feature components used to describe the shape. A straightforward treatment is to project tracked shapes into a Principal Component Analysis (PCA) subspace. However, this approach cannot take advantage of the measurement uncertainty and is therefore not complete. In many instances, measurement noise is heteroscedastic in nature (i.e., both anisotropic and inhomogeneous). There is a need for an object tracking method that can fuse motion estimates from multiple-appearance models and which can effectively take into account uncertainty.